Inherent directionality explains the lack of feedback loops in empirical networks Domínguez-García, Virginia Pigolotti, Simone Muñoz Martínez, Miguel Ángel Complexity Computational science We explore the hypothesis that the relative abundance of feedback loops in many empirical complex networks is severely reduced owing to the presence of an inherent global directionality. Aimed at quantifying this idea, we propose a simple probabilistic model in which a free parameter γ controls the degree of inherent directionality. Upon strengthening such directionality, the model predicts a drastic reduction in the fraction of loops which are also feedback loops. To test this prediction, we extensively enumerated loops and feedback loops in many empirical biological, ecological and socio-technological directed networks. We show that, in almost all cases, empirical networks have a much smaller fraction of feedback loops than network randomizations. Quite remarkably, this empirical finding is quantitatively reproduced, for all loop lengths, by our model by fitting its only parameter γ. Moreover, the fitted value of γ correlates quite well with another direct measurement of network directionality, performed by means of a novel algorithm. We conclude that the existence of an inherent network directionality provides a parsimonious quantitative explanation for the observed lack of feedback loops in empirical networks. 2015-02-19T12:52:18Z 2015-02-19T12:52:18Z 2014 info:eu-repo/semantics/article Domínguez-García, V.; Pigolotti, S.; Muñoz Martínez, M.A. Inherent directionality explains the lack of feedback loops in empirical networks. Scientific Reports, 4: 7497 (2014). [http://hdl.handle.net/10481/34877] 2045-2322 http://hdl.handle.net/10481/34877 10.1038/srep07497 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Nature Publishing Group